Extend synthetic difference-in-differences to staggered adoption, where units adopt treatment at different times, and apply it in Stata to parliamentary gender quotas across 119 countries — deriving the per-cohort estimator, its aggregation into the overall ATT, the modern sdid_event event study, and bootstrap, jackknife, and placebo inference.
Introduce and derive synthetic difference-in-differences, then apply it to California's Proposition 99 — comparing SDID with the original difference-in-differences and synthetic control (synth2), and how to run placebo inference with a single treated unit.
When the 'treatment' is a point in space, distance becomes the running variable. We walk through the parametric ring DiD and a data-driven nonparametric alternative, first on a simulated world with a known answer, then on Linden and Rockoff's home-prices study, and reconcile a parametric −5.78 % with a nonparametric −20.6 %.
A case study on the Affordable Care Act's Medicaid expansion --- working through 2x2 cell-means, TWFE, covariate-adjusted DRDID, 2xT and Callaway-Sant'Anna staggered event studies, and HonestDiD sensitivity --- to show how population weighting changes the target parameter when the units are regions of very different sizes.
Learn Difference-in-Differences (DiD) in Python using PyFixest and Great Tables. Covers the 2x2 design, TWFE regression, inference comparison, publication-quality tables, event studies, and parallel trends testing based on Corral and Yang (2024).
Learn Difference-in-Differences (DiD) in Stata using a case study of an after-school tutoring program. Covers the 2x2 design, TWFE regression, event studies, and parallel trends testing based on Corral and Yang (2024).
A guide to Difference-in-Differences with staggered treatment --- from TWFE pitfalls through Callaway-Sant'Anna group-time ATTs, doubly robust estimation, and HonestDiD sensitivity analysis --- applied to minimum wage effects on teen employment.
Assess how robust difference-in-differences results are to violations of parallel trends using the honestdid package in Stata, progressing from a simple 2x2 DiD to multi-period event studies with relative magnitudes and smoothness restrictions
Estimating causal treatment effects using Difference-in-Differences with the diff-diff package, from the classic 2x2 design through staggered adoption with Callaway-Sant'Anna and HonestDiD sensitivity analysis